A new leak location method based on leakage acoustic waves for oil and gas pipelines

In order to study a new leak detection and location method for oil and natural gas pipelines based on acoustic waves, the propagation model is established and modified. Firstly, the propagation law in theory is obtained by analyzing the damping impact factors which cause the attenuation. Then, the dominant-energy frequency bands of leakage acoustic waves are obtained through experiments by wavelet transform analysis. Thirdly, the actual propagation model is modified by the correction factor based on the dominant-energy frequency bands. Then a new leak detection and location method is proposed based on the propagation law which is validated by the experiments for oil pipelines. Finally, the conclusions and the method are applied to the gas pipelines in experiments. The results indicate: the modified propagation model can be established by the experimental method; the new leak location method is effective and can be applied to both oil and gas pipelines and it has advantages over the traditional location method based on the velocity and the time difference. Conclusions can be drawn that the new leak detection and location method can effectively and accurately detect and locate the leakages in oil and natural gas pipelines.     

Analysis of the Trend and Seasonal Cycle of Carbon Monoxide Concentrations in an Urban Area (4 pp)

Background, Aim and Scope Air quality is an field of major concern in large cities. This problem has led administrations to introduce plans and regulations to reduce pollutant emissions. The analysis of variations in the concentration of pollutants is useful when evaluating the effectiveness of these plans. However, such an analysis cannot be undertaken using standard statistical techniques, due to the fact that concentrations of atmospheric pollutants often exhibit a lack of normality and are autocorrelated. On the other hand, if long-term trends of any pollutant’s emissions are to be detected, meteorological effects must be removed from the time series analysed, due to their strong masking effects. Materials and Methods The application of statistical methods to analyse temporal variations is illustrated using monthly carbon monoxide (CO) concentrations observed at an urban site. The sampling site is located at a street intersection in central Valencia (Spain) with a high traffic density. Valencia is the third largest city in Spain. It is a typical Mediterranean city in terms of its urban structure and climatology. The sampling site started operation in January 1994 and monitored CO ground level concentrations until February 2002. Its geographic coordinates are W0°22′52″ N39°28′05″ and its altitude is 11 m. Two nonparametric trend tests are applied. One of these is robust against serial correlation with regards to the false rejection rate, when observations have a strong persistence or when the sample size per month is small. A nonparametric analysis of the homogeneity of trends between seasons is also discussed. A multiple linear regression model is used with the transformed data, including the effect of meteorological variables. The method of generalized least squares is applied to estimate the model parameters to take into account the serial dependence of the residuals of this model. This study also assesses temporal changes using the Kolmogorov-Zurbenko (KZ) filter. The KZ filter has been shown to be an effective way to remove the influence of meteorological conditions on O₃ and PM to examine underlying trends. Results The nonparametric tests indicate a decreasing, significant trend in the sampled site. The application of the linear model yields a significant decrease every twelve months of 15.8% for the average monthly CO concentration. The 95% confidence interval for the trend ranges from 13.9% to 17.7%. The seasonal cycle also provides significant results. There are no differences in trends throughout the months. The percentage of CO variance explained by the linear model is 90.3%. The KZ filter separates out long, short-term and seasonal variations in the CO series. The estimated, significant, long-term trend every year results in 10.3% with this method. The 95% confidence interval ranges from 8.8% to 11.9%. This approach explains 89.9% of the CO temporal variations. Discussion The differences between the linear model and KZ filter trend estimations are due to the fact that the KZ filter performs the analysis on the smoothed data rather than the original data. In the KZ filter trend estimation, the effect of meteorological conditions has been removed. The CO short-term componentis attributable to weather and short-term fluctuations in emissions. There is a significant seasonal cycle. This component is a result of changes in the traffic, the yearly meteorological cycle and the interactions between these two factors. There are peaks during the autumn and winter months, which have more traffic density in the sampled site. There is a minimum during the month of August, reflecting the very low level of vehicle emissions which is a direct consequence of the holiday period. Conclusions The significant, decreasing trend implies to a certain extent that the urban environment in the area is improving. This trend results from changes in overall emissions, pollutant transport, climate, policy and economics. It is also due to the effect of introducing reformulated gasoline. The additives enable vehicles to burn fuel with a higher air/fuel ratio, thereby lowering the emission of CO. The KZ filter has been the most effective method to separate the CO series components and to obtain an estimate of the long-term trend due to changes in emissions, removing the effect of meteorological conditions. Recommendations and Perspectives Air quality managers and policy-makers must understand the link between climate and pollutants to select optimal pollutant reduction strategies and avoid exceeding emission directives. This paper analyses eight years of ambient CO data at a site with a high traffic density, and provides results that are useful for decision-making. The assessment of long-term changes in air pollutants to evaluate reduction strategies has to be done while taking into account meteorological variability     

埋地钢质输水管道的内外防腐选择

本文通过目前比较常见的几种内外防腐方式及材料的性能和优缺点 ,对埋地钢质输水管道的防腐选择提供了参考意见     

铜川新区建设生态城市发展模式研究

以可持续发展为指导思想，结合生态城市的内涵，提出城乡一体化和生态生产的城市发展思路，建立了土地容量与人口适宜容量数学模型。根据铜川新区具有的自然优势，从自然—社会—经济复合生态角度，针对铜川新区的土地容量与人口适宜容量，探讨了将铜川新区建设成为一个环境优美、人口适宜的生态城市的发展模式。     

大连声环境质量的灰色预测研究

根据1999年～2003年大连市区声环境质量的监测数据，采用两次残差修正GM（1，1）对2010年前声环境质量进行预测结果表明，修正后模型均为一级模型，进行中、短期预测精度高。预测结果可为城市噪声污染防治提供科学的依据。     

参窝水库水体自净能力及水质变化规律研究

根据参窝水库近几年的水质监测资料，对参窝水库的水体自净能力及水质变化规律进行了较深入的研究、计算出主要污染物在参窝水库的自净率，分析了参窝水库水质变化规律，为参窝水库的水污染防治提供了科学的依据。     

家庭中水利用装置系统的设计

结合我国家庭中水的利用现状,设计了一套具有广泛应用性的家庭中水利用装置系统,并实现了相应的自动化控制方案。同时,详细介绍了系统的设计思路与实现的主要功能,并对装置系统的有关问题进行了深入的探讨。     

浅析沘江水体污染

Bi江水质已为劣V类，主要污染物为Pb,Zn,Cd,As,Hg等重金属，主要污染源是上游的兰坪铅锌矿。水污染已对Bi江两岸人民的生活，生产和健康造成极大影响。目前条件下一要侧重生物多样性保护，治理水土流失，恢复和重建生态环境，二要侧重工业污染防治。     

高邮市基本农田环境质量监测与评价

根据近两年高邮市试点工作经验，介绍基本农田分等定级的环境监测与评价方法，认为积极开展农田环境监测并进行科学评价是基本农田保护的重要工作内容。     

水质模拟及趋势分析

根据洛河、伊河河流及流经地区特征 ,选择具有代表性的断面 ,选用一维河流有限差分水质模型 ,计算洛河、伊河 1 995～2 0 0 0年主要断面污染物浓度 ,并对其进行评价。采用季节性肯达尔检验法 ,对计算出的洛河、伊河 1 995～ 2 0 0 0年主要断面污染物浓度进行趋势分析 ,并分析其变化特征及形成原因